Neural nets versus conventional techniques in credit scoring in Egyptian banking

Abdou, HAH; Pointon, J; El-Masry, A

Abstract

Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank’s personal loans’ data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.

Citation

Abdou, H., Pointon, J., & El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert systems with applications, 35(3), 1275-1292. https://doi.org/10.1016/j.eswa.2007.08.030

• Journal Article Type: Article
• Publication Date: Oct 1, 2008
• Deposit Date: Dec 3, 2009
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 3
• Pages: 1275-1292
• DOI: https://doi.org/10.1016/j.eswa.2007.08.030
• Keywords: Neural nets; Conventional techniques; Banking; Credit scoring
• Publisher URL: http://dx.doi.org/10.1016/j.eswa.2007.08.030